Abstract: A system may comprise a data interpolation platform that comprises a data collector, a pre-processor, an artificial intelligence (AI) engine, an interactive graphical user interface (GUI) engine, a data monitor and one or more servers. The data collector may be configured to collect, from among one or more data sources, data and information (“collected data”). The AI engine may be configured to generate, train, validate, test and/or deploy one or more AI models. The interactive GUI engine may be configured to generate and dynamically update an interactive GUI. The one or more servers may comprise one or more processors, a memory and computer-readable instructions that, when executed by the one or more processors, cause the data interpolation platform to determine, display and dynamically update predictive and interpolated pricing data.

Abstract: Integrated circuit structures having combined links for uniform grid metal gate and trench contact cut are described. A structure includes a dielectric sidewall spacer between a gate electrode and a conductive trench contact. First and second parallel dielectric cut plug structures extend through the gate electrode, through the dielectric sidewall spacer, and through the conductive trench contact. The first dielectric cut plug has a first recess laterally adjacent to first and second portions of the gate electrode. The second dielectric cut plug has a second recess laterally adjacent to first and second portions of the conductive trench contact. A conductive link is in the first recess, in the second recess, and continuous between the first recess and the second recess.

Abstract: A system may comprise a data interpolation platform that comprises a data collector, a pre-processor, an artificial intelligence (AI) engine, an interactive graphical user interface (GUI) engine, a data monitor and one or more servers. The data collector may be configured to collect, from among one or more data sources, data and information (“collected data”). The AI engine may be configured to generate, train, validate, test and/or deploy one or more AI models. The interactive GUI engine may be configured to generate and dynamically update an interactive GUI. The one or more servers may comprise one or more processors, a memory and computer-readable instructions that, when executed by the one or more processors, cause the data interpolation platform to determine, display and dynamically update predictive and interpolated pricing data.

Abstract: A range finding apparatus uses a light-receiving device in which a first pixel having a first sensitivity and a second pixel having a second sensitivity that is lower than the first sensitivity are two-dimensionally arranged. The range finding apparatus measures time periods from a predetermined time until times when light is incident on each of the first pixel and the second pixel, and computes distance information for the first pixel and the second pixel based on the measured time periods. The measurement resolution used to measure the time period for the second pixel is lower than a measurement resolution used to measure the time period for the first pixel.

Abstract: A system may comprise a data interpolation platform that comprises a data collector, a pre-processor, an artificial intelligence (AI) engine, an interactive graphical user interface (GUI) engine, a data monitor and one or more servers. The data collector may be configured to collect, from among one or more data sources, data and information (“collected data”). The AI engine may be configured to generate, train, validate, test and/or deploy one or more AI models. The interactive GUI engine may be configured to generate and dynamically update an interactive GUI. The one or more servers may comprise one or more processors, a memory and computer-readable instructions that, when executed by the one or more processors, cause the data interpolation platform to determine, display and dynamically update predictive and interpolated pricing data.

Abstract: A method is provided comprising: monitoring, by a symbol context manager, context switch events that are generated in a virtual machine, and updating a symbol space map based on the context switch events; receiving, by the symbol context manager, a request to provide a symbol space of the virtual machine, the request being generated by a symbol database interface in response to a symbol query that is received at the symbol database interface from a debugger that is debugging the virtual machine, the symbol query being associated with a symbol that is part of the symbol space; and providing, by the symbol context manager, an indication of the symbol space of the virtual machine, the indication of the symbol space being provided based on the symbol space map.

Abstract: A system and method for defining a relational schema for a non-relational database are disclosed. In one embodiment, the system identifies a non-relational database; obtains a data structure of the non-relational database; determines a relational schema type; generates a relational schema based on the determined schema type; determines whether the relational schema includes a plurality of relational tables; and responsive to determining that the relational schema includes a plurality of relational tables, defines a relation between at least two of the relational tables in the relational schema.

Abstract: A system may comprise a data interpolation platform that comprises a data collector, a pre-processor, an artificial intelligence (AI) engine, an interactive graphical user interface (GUI) engine, a data monitor and one or more servers. The data collector may be configured to collect, from among one or more data sources, data and information (“collected data”). The AI engine may be configured to generate, train, validate, test and/or deploy one or more AI models. The interactive GUI engine may be configured to generate and dynamically update an interactive GUI. The one or more servers may comprise one or more processors, a memory and computer-readable instructions that, when executed by the one or more processors, cause the data interpolation platform to determine, display and dynamically update predictive and interpolated pricing data.

Abstract: A system may comprise a data interpolation platform that comprises a data collector, a pre-processor, an artificial intelligence (AI) engine, an interactive graphical user interface (GUI) engine, a data monitor and one or more servers. The data collector may be configured to collect, from among one or more data sources, data and information (“collected data”). The AI engine may be configured to generate, train, validate, test and/or deploy one or more AI models. The interactive GUI engine may be configured to generate and dynamically update an interactive GUI. The one or more servers may comprise one or more processors, a memory and computer-readable instructions that, when executed by the one or more processors, cause the data interpolation platform to determine, display and dynamically update predictive and interpolated pricing data.

Abstract: A system may comprise a data interpolation platform that comprises a data collector, a pre-processor, an artificial intelligence (AI) engine, an interactive graphical user interface (GUI) engine, a data monitor and one or more servers. The data collector may be configured to collect, from among one or more data sources, data and information (“collected data”). The AI engine may be configured to generate, train, validate, test and/or deploy one or more AI models. The interactive GUI engine may be configured to generate and dynamically update an interactive GUI. The one or more servers may comprise one or more processors, a memory and computer-readable instructions that, when executed by the one or more processors, cause the data interpolation platform to determine, display and dynamically update predictive and interpolated pricing data.

Abstract: Methods, apparatus, systems and articles of manufacture to securely handle chip card data are disclosed. An example method includes providing, by executing an instruction with a first processor of a client device, an application programming interface (API) in a web client of the client device, in response to detecting, in the web client at the client device, a query from a server for card data, operating, by executing an instruction with the first processor of the client device, the API in the web client at the client device to obtain the card data stored on a chip of a chip card communicatively coupled to the client device, and sending, by executing an instruction with the first processor of the client device, the card data to the server.

Abstract: A system and method for defining a relational schema for a non-relational database are disclosed. In one embodiment, the system identifies a non-relational database; obtains a data structure of the non-relational database; determines a relational schema type; generates a relational schema based on the determined schema type; determines whether the relational schema includes a plurality of relational tables; and responsive to determining that the relational schema includes a plurality of relational tables, defines a relation between at least two of the relational tables in the relational schema.

Abstract: The application describes a device for providing a medicament includes a dispensing compartment, a medicament storage compartment adapted to store a medicament packaging containing the medicament, a transport mechanism adapted to transport the medicament packaging containing the medicament from the medicament storage compartment to the dispensing compartment, an opening station disposed within the dispensing compartment and adapted to open the medicament packaging containing the medicament while on the transport mechanism, an extruder disposed within the dispensing compartment and adapted to remove the medicament from the medicament packaging, and an outlet from which a user can receive the medicament. The device may provide medicament at a desired scheduled interval. The device may be adapted to open a medicament packaging and remove a medicament from the medicament packaging.

Abstract: A system and method for defining a relational schema for a non-relational database are disclosed. In one embodiment, the system identifies a non-relational database; obtains a data structure of the non-relational database; determines a relational schema type; generates a relational schema based on the determined schema type; determines whether the relational schema includes a plurality of relational tables; and responsive to determining that the relational schema includes a plurality of relational tables, defines a relation between at least two of the relational tables in the relational schema.

Abstract: Methods, apparatus, systems and articles of manufacture to securely handle chip card data are disclosed. An example method includes providing, by executing an instruction with a first processor of a client device, an application programming interface (API) in a web client of the client device, in response to detecting, in the web client at the client device, a query from a server for card data, operating, by executing an instruction with the first processor of the client device, the API in the web client at the client device to obtain the card data stored on a chip of a chip card communicatively coupled to the client device, and sending, by executing an instruction with the first processor of the client device, the card data to the server.

Abstract: A method is provided comprising: monitoring, by a symbol context manager, context switch events that are generated in a virtual machine, and updating a symbol space map based on the context switch events; receiving, by the symbol context manager, a request to provide a symbol space of the virtual machine, the request being generated by a symbol database interface in response to a symbol query that is received at the symbol database interface from a debugger that is debugging the virtual machine, the symbol query being associated with a symbol that is part of the symbol space; and providing, by the symbol context manager, an indication of the symbol space of the virtual machine, the indication of the symbol space being provided based on the symbol space map.

Abstract: A mobile application is provided that provides intelligent recommendations based on the knowledge of where the user has been, and what venues the user would like to visit. Further, such an application may be capable of determining where people in a user's social network have been and what venue locations these related users would like to visit. Also, in another implementation, the application may be capable of determining where people with similar taste have been, and where they would like to go. Some or all of this information may be used by a mobile application that provides recommendations to a user. For instance, in one implementation, a user having a mobile device such as a cell phone wishes to locate a venue based on one or more parameters, and some or all of this information may be used to order to rank recommendations with the interface.

Abstract: Methods, apparatus, systems and articles of manufacture to securely handle chip card data are disclosed. An example method includes providing, by executing an instruction with a first processor of a client device, an application programming interface (API) in a web client of the client device, in response to detecting, in the web client at the client device, a query from a server for card data, operating, by executing an instruction with the first processor of the client device, the API in the web client at the client device to obtain the card data stored on a chip of a chip card communicatively coupled to the client device, and sending, by executing an instruction with the first processor of the client device, the card data to the server.

Abstract: In one embodiment, a method includes identifying one or more place-entity nodes in a heterogeneous graph, wherein the heterogenous graph comprises place-entity nodes, user nodes, and n-gram nodes, and wherein each place-entity node corresponds to a place-entity associated with a particular geographic location, assigning, for each identified place-entity node, an initial quality-score for the place-entity node, and calculating, for each identified place-entity node, a final quality-score for the identified place-entity node, wherein each final quality-score is calculated by iteratively propagating the initial quality-scores corresponding to the identified place-entity nodes, respectively, through the place-entity nodes, n-gram nodes, and user nodes of the heterogeneous graph until the quality-scores associated with the place-entity nodes, n-gram nodes, and user nodes reach convergence.

Abstract: A mobile application is provided that provides intelligent recommendations based on the knowledge of where the user has been, and what venues the user would like to visit. Further, such an application may be capable of determining where people in a user's social network have been and what venue locations these related users would like to visit. Also, in another implementation, the application may be capable of determining where people with similar taste have been, and where they would like to go. Some or all of this information may be used by a mobile application that provides recommendations to a user. For instance, in one implementation, a user having a mobile device such as a cell phone wishes to locate a venue based on one or more parameters, and some or all of this information may be used to order to rank recommendations with the interface.